Jupyter is a web-based, interactive computing environment that supports many commonly-used programming languages. It has been widely adopted in the CS education community and is now rapidly expanding to other STEM disciplines due to the growing integration of programming in STEM education. However, unlike other educational platforms, there is currently no integrated way to capture, analyze, and visualize student interaction data in Jupyter notebooks. This means that teachers have limited to no visibility into student activity, preventing them from drawing insights from these data and providing timely interventions on the fly. In this paper, we present Jupyter Analytics, an end-to-end solution for teachers to collect, analyze, and visualize both synchronous and asynchronous learning activities in Jupyter. The Jupyter Analytics system consists of two JupyterLab extensions connected via a cloud-based backend. On the student side, we introduce the Jupyter Analytics Telemetry extension to anonymously capture students' interaction activity with more structure and higher granularity than log data. On the teacher side, we introduce the Jupyter Analytics Dashboard extension, which visualizes real-time student data directly in the notebook interface. The Jupyter Analytics system was developed through an iterative co-design process with university instructors and teaching assistants, and has been implemented and tested in several university STEM courses. We report two use cases where Jupyter Analytics impacted teaching and learning in the context of exercise sessions, and discuss the potential value of our tools for CS education.

The recent advances in quantum information processing, sensing, and communications are surveyed with the objective of identifying the associated knowledge gaps and formulating a roadmap for their future evolution. Since the operation of quantum systems is prone to the deleterious effects of decoherence, which manifests itself in terms of bit-flips, phase-flips, or both, the pivotal subject of quantum error mitigation is reviewed both in the presence and absence of quantum coding. The state of the art, knowledge gaps, and future evolution of quantum machine learning (QML) are also discussed, followed by a discourse on quantum radar systems and briefly hypothesizing about the feasibility of integrated sensing and communications (ISAC) in the quantum domain (QD). Finally, we conclude with a set of promising future research ideas in the field of ultimately secure quantum communications with the objective of harnessing ideas from the classical communications field.

Symmetric positive definite (SPD) matrices have been widely used as Riemannian feature descriptors in various scientific fields, due to their capacity to encode effective manifold-valued representations. Inspired by the architectural principles of Euclidean deep learning, the emerging SPD neural networks have achieved more robust signal classification. Among these advancements, Riemannian batch normalization (RBN) based on the affine-invariant Riemannian metric (AIRM) has emerged as a key technique for enhancing the learning capability of SPD-based networks. Nevertheless, the reliance of singular value decomposition (SVD) makes this metric relatively unstable for the computation of SPD matrices, especially for the ill-conditioned case. To address this limitation, we propose a novel RBN algorithm based on the recently introduced log-Cholesky metric (LCM), which leverages Cholesky decomposition. Unlike AIRM, the LCM offers enhanced numerical stability and allows for more efficient computation. Specifically, the LCM-based Riemannian operators such as Fr $\acute {\mathrm {e}}$ chet mean and parallel transport (PT) are much simpler than those of AIRM, and both have closed forms. Besides, since LCM is the pullback metric from the Cholesky manifold via Cholesky decomposition, the LCM-based RBN on the SPD manifold can be computed in the Cholesky manifold, further boosting the efficiency. Extensive experiments conducted on four benchmarking datasets certify the effectiveness of our proposed algorithm. The source code is now available at: https://github.com/jjscc/CBN.git.

The generalization of Euclidean network paradigm to the Riemannian manifolds has attracted much attention for offering useful geometric representations in processing manifold-valued data in recent years. However, the information degradation during data compression mapping hinders Riemannian networks from going deeper, and there are very few solutions specifically designed for this problem. Given the remarkable success of deep Residual learning in Euclidean networks, a novel Riemannian residual learning mechanism (RRLM) is proposed in the context of Symmetric Positive Definite (SPD) manifolds, enabling the characterization of deep spatiotemporal features while preserving the manifold properties. Based on RRLM, a stack of SPD manifold-constrained residual-like blocks is designed on the tail of the original SPDNet(backbone) for the sake of conducting deep Riemannian residual learning. For simplicity, we refer to the network architecture introduced above as Riemannian residual SPD network (ResSPDNet). The experimental results achieved on three types of visual classification tasks, i.e., facial emotion recognition, drone recognition, and action recognition, demonstrate that our method can achieve improved accuracy with a deepened network structure.

Although several studies suggested considering gender in online learning, the literature about how male and female students would behave is fragmented. Little attention has been paid to the effect of gender on online learning behavioural patterns. This study aimed at investigating the roles of gender in online learning behaviours by analyzing the gender-related differences of students’ online learning behavioural patterns. We used the case study approach with descriptive statistical analysis, lag sequential analysis, and temporal log data analysis to investigate gender-related differences in students’ online learning behaviours. The results indicated no significant difference in the counts of occurrence of online single learning behaviours between female and male students. However, differences were observed in online learning behaviour patterns and how the online learning activities were performed over time. Females were more active in learning behaviours associated with achievement reports and peer list viewing. They tended to view their achievement reports before starting the main course learning activities, indicating that female students might be achievement-oriented. The findings provide further insights from a temporal perspective about how gender is associated with online learning. Implications on designing personalized online learning interventions based on considering gender-related differences are also discussed.

Despite several studies highlighted the importance of considering gender in online learning, the current literature about how male and female students would behave is still fragmented. Additionally, little attention has been paid to investigating the impact of gender on online learning behavioral patterns. This study applies lag sequential analysis (LSA) to investigate gender-related difference in the behavioral patterns of 116 students in an online course for six weeks. The obtained results indicated that overall there is no significant difference in the frequency of online learning behaviors between female and male students. However, the LSA showed that males and females demonstrated different transitional patterns in their online learning behaviors. Female behaviors were more coherently linked to each other. In contrast, some of the male behaviors were relatively isolated without significant antecedents and consequences, calling for learning supports. Also, females tended to view their achievement reports before starting the main course activities, showing that female students were more achievement-oriented. The findings provide explanations about how gender affects online learning and implications on how to design personalized online learning interventions based on considering gender-related differences.

The rapid development of technology has changed the traditional education and teaching methods. It has become a hot topic in the field of education to emphasize the cultivation of multi-ability collaborative learning. From the perspective of learning analysis, the research focuses on the design tasks of collaborative learning. Taking part of the participants of 2019 Global Design Competition for Future Education as the research object, the LSA is used to explore the differences in the sequence of behavior activities of the participants in the early and later stages of completing the design projects. The results show that in the early stage, sharing and argumentation are the core, emotional communication is the focus; in the later stage, argumentation, summary and modification are the core, and then the questions, and answers are the focus.

Remote sensing image characteristics, bandwidth limitation and security are the most important consideration to remote sensing image issue. For more effectively issue image information, a strategy is adopted to divide Web users into C/S (client/server) mode user and B/S (browser/server) mode user. The complex data operations and system functions, coordinate scope query of area of interest by WebGIS or metadata can be used by C/S mode user; But the B/S mode user which is used by common users can only obtain retrieval service, such as image samples retrieval, image metadata retrieval and other related retrieval or acquiring of relatively little quantity of data. Of course, the framework that DEM, vector, raster and other related data are integrated also are discussed. Then, spatial data sharing and exchanging based on XML are proposed. Finally, a system of B/S mode is designed and implemented.